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Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems
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Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (25 April 2025) | Viewed by 4363

Special Issue Editor

Dr. Tuan Nguyen Gia

E-Mail Website
Guest Editor
Department of Future Technologies, University of Turku, 20500 Turku, Finland
Interests: edge computing; fog computing; edge-AI; autonomous systems; autonomous vehicles; FPGA; swarm of drones; co-robot; energy efficiency and e-health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, edge and fog computing has drawn attractions from many industries and academia. These computing paradigms not only address the need for low-latency and real-time applications by performing computing at the edge of a network but also proffer capabilities to improve the quality of service. Nowadays, edge and fog computing have further evolved with the integration of Artificial Intelligence (AI), offering more innovative solutions and architectures across various domains including IoT applications and smart distributed systems. These solutions and architectures bring AI closer to the data sources to overcome the limitations of centralized systems using cloud computing.

This special issue, titled “Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems,” invites researchers, scholars, and industry experts to contribute their innovative work and perspectives. We aim to explore key challenges and novel solutions in the intersection of edge/fog computing and artificial, particularly as they apply to IoT applications and smart distributed systems. Submissions are welcomed but not limited to the following areas:

  • New paradigms, concepts, and architectures in Edge-AI systems.
  • Distributed learning and model training using decentralized algorithms
  • AI model deployment at the Edge/Fog
  • On devices inference for distributed decision making
  • Data processing and information extraction at smart edge devices
  • Autonomous contextual awareness with Edge AI
  • Decentralized algorithms/approaches for scalability, interoperability, and adaptability in distributed systems
  • Distributed fault tolerance approaches used for maintaining quality of service
  • Distributed computation offloading algorithms

Dr. Nguyen Gia Tuan
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • edge computing
  • fog computing
  • decentralization
  • internet-of-things
  • edge AI
  • smart distributed systems

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Published Papers (4 papers)

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Research

20 pages, 4917 KiB  
Article
Comprehensive Forecasting of Electrical Quantities in an Educational Building via Artificial Intelligence-Driven Distributed Measurement System
by Virginia Negri, Roberto Tinarelli, Lorenzo Peretto and Alessandro Mingotti
Sensors 2025, 25(8), 2456; https://doi.org/10.3390/s25082456 - 14 Apr 2025
Viewed by 339
Abstract
Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend in [...] Read more.
Recent environmental concerns have heightened attention toward new solutions across all fields to mitigate human impact. The power system community is also deeply committed to addressing this issue, with research increasingly focused on sustainable practices. For instance, there is a growing trend in designing new buildings to be net-zero emitters, while older structures are being retrofitted for energy efficiency to achieve similar goals. To this purpose, the study aims to enhance the energy management capabilities of an educational building by implementing a smart infrastructure. Equipped with photovoltaic panels and a distributed measurement system, the building captures voltage and current data and calculates power. These electrical quantities are then forecasted through an AI-driven framework that manages the data. The paper details the AI model used, including its experimental validation. The results show that the system provides reliable forecasts of electrical parameters. The evaluation of the distributed measurement system and the collected data offers valuable insights, which support more informed actions for optimizing energy management and system performance. A key novelty of this study lies in the exploration of model generalization across measurement nodes. This approach is supported by the correlation analysis of data, which highlights the potential for accurate predictions in case of data gaps. Moreover, the ease of deployment and the practical application of the system were highlighted as key factors for scalability, allowing for potential adaptation in similar infrastructures. Full article
(This article belongs to the Special Issue Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems)
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26 pages, 8705 KiB  
Article
Person Re-Identification with Attribute-Guided, Robust-to-Low-Resolution Drone Footage Considering Fog/Edge Computing
by Bongjun Kim, Sunkyu Kim, Seokwon Park and Junho Jeong
Sensors 2025, 25(6), 1819; https://doi.org/10.3390/s25061819 - 14 Mar 2025
Viewed by 430
Abstract
In aerial surveillance using drones, person re-identification (ReID) is crucial for public safety. However, low resolutions in drone footage often leads to a significant drop in ReID performance of subjects. To investigate this issue, rather than relying solely on real-world datasets, we employed [...] Read more.
In aerial surveillance using drones, person re-identification (ReID) is crucial for public safety. However, low resolutions in drone footage often leads to a significant drop in ReID performance of subjects. To investigate this issue, rather than relying solely on real-world datasets, we employed a synthetic dataset that systematically captures variations in drone altitude and distance. We also utilized an eXplainable Artificial Intelligence (XAI) framework to analyze how low resolutions affect ReID. Based on our findings, we propose a method that improves ReID accuracy by filtering out attributes that are not robust in low-resolution environments and retaining only those features that remain reliable. Experiments on the Market1501 dataset show a 6.59% percentage point improvement in accuracy at a 16% resolution scale. We further discuss the effectiveness of our approach in drone-based aerial surveillance systems under Fog/Edge Computing paradigms. Full article
(This article belongs to the Special Issue Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems)
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25 pages, 6169 KiB  
Article
Elephant Sound Classification Using Deep Learning Optimization
by Hiruni Dewmini, Dulani Meedeniya and Charith Perera
Sensors 2025, 25(2), 352; https://doi.org/10.3390/s25020352 - 9 Jan 2025
Cited by 1 | Viewed by 1507
Abstract
Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing. Our focus lies [...] Read more.
Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation. This study addresses elephant sound classification utilizing raw audio processing. Our focus lies on exploring lightweight models suitable for deployment on resource-costrained edge devices, including MobileNet, YAMNET, and RawNet, alongside introducing a novel model termed ElephantCallerNet. Notably, our investigation reveals that the proposed ElephantCallerNet achieves an impressive accuracy of 89% in classifying raw audio directly without converting it to spectrograms. Leveraging Bayesian optimization techniques, we fine-tuned crucial parameters such as learning rate, dropout, and kernel size, thereby enhancing the model’s performance. Moreover, we scrutinized the efficacy of spectrogram-based training, a prevalent approach in animal sound classification. Through comparative analysis, the raw audio processing outperforms spectrogram-based methods. In contrast to other models in the literature that primarily focus on a single caller type or binary classification that identifies whether a sound is an elephant voice or not, our solution is designed to classify three distinct caller-types namely roar, rumble, and trumpet. Full article
(This article belongs to the Special Issue Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems)
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14 pages, 3315 KiB  
Article
Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System
by Shing-Hong Liu, Bo-Yan Wu, Xin Zhu and Chiun-Li Chin
Sensors 2024, 24(23), 7830; https://doi.org/10.3390/s24237830 - 7 Dec 2024
Viewed by 1365
Abstract
Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP [...] Read more.
Blood pressure (BP) measurement is a major physiological information for people with cardiovascular diseases, such as hypertension, heart failure, and atherosclerosis. Moreover, elders and patients with kidney disease and diabetes mellitus also are suggested to measure their BP every day. The cuffless BP measurement has been developed in the past 10 years, which is comfortable to users. Now, ballistocardiogram (BCG) and impedance plethysmogram (IPG) could be used to perform the cuffless BP measurement. Thus, the aim of this study is to realize edge computing for the BP measurement in real time, which includes measurements of BCG and IPG signals, digital signal process, feature extraction, and BP estimation by machine learning algorithm. This system measured BCG and IPG signals from a bodily weight-fat scale with the self-made circuits. The signals were filtered to reduce the noise and segmented by 2 s. Then, we proposed a flowchart to extract the parameter, pulse transit time (PTT), within each segment. The feature included two calibration-based parameters and one calibration-free parameter was used to estimate BP with XGBoost. In order to realize the system in STM32F756ZG NUCLEO development board, we limited the hyperparameters of XGBoost model, including maximum depth (max_depth) and tree number (n_estimators). Results show that the error of systolic blood pressure (SBP) and diastolic blood pressure (DBP) in server-based computing are 2.64 ± 9.71 mmHg and 1.52 ± 6.32 mmHg, and in edge computing are 2.2 ± 10.9 mmHg and 1.87 ± 6.79 mmHg. This proposed method significantly enhances the feasibility of bodily weight-fat scale in the BP measurement for effective utilization in mobile health applications. Full article
(This article belongs to the Special Issue Integration of Edge/Fog Artificial Intelligence into Smart Distributed Systems)
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